A Framework for Fixed Complexity Breadth-First MIMO Detection

Milliner, D.L.; Zimmermann, Ernesto; Barry, John R.; Fettweis, Gerhard

doi:10.1109/isssta.2008.31

Cited by 9 publications

(4 citation statements)

References 22 publications

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“…Then, we can choose the terminal node with the smallest (˜) as the output˜. Note that this tree structure is different from the one for sphere-decoding [8] or the M-algorithm in sequential decoding [17]:…”

Section: A Tree-search Algorithmsmentioning

confidence: 99%

Designing Low-Complexity Detectors Based on Seysen's Algorithm

Zhang

Swami

2010

IEEE Trans. Wireless Commun.

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Lattice reduction (LR) has been applied to linear equalizers and decision feedback equalizers to improve their performance. Recently, Seysen's algorithm (SA) has been proposed as an alternative LR method to the well-documented LLL algorithm. In this paper, we first provide a tree-search implementation method of SA which enables flexible performancecomplexity trade-offs. Based on this tree structure, SA-aided hard-output and soft-output detectors are proposed to enhance performance. The diversity and complexity of the proposed methods are also studied. Thorough comparisons of SA and other LR algorithms are provided. Our analysis of complexity and numerical simulations provide insights that are useful to system designers.

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Section: A Tree-search Algorithmsmentioning

confidence: 99%

Designing Low-Complexity Detectors Based on Seysen's Algorithm

Zhang

Swami

2010

IEEE Trans. Wireless Commun.

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“…An efficient implementation of the ML detector has been actively researched such as sphere decoder (SD) [5], fixed-complexity SD (FSD) [6], K-Best [7], coset-based lattice detector [8], lattice reduction detector [9], smart candidate adding method [10] and geometric detector [11], while they are still quite complicated especially for large M and/or N S .…”

Section: Introductionmentioning

confidence: 99%

Improved MIMO SIC Detection Exploiting ML Criterion

Choi

Lee

Lou

2011

2011 IEEE Vehicular Technology Conference (VTC Fall)

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In this paper, we propose an improved MIMO successive interference cancellation (SIC) detector taking maximumlikelihood (ML) criterion into account. Applying the ML criterion to multiple candidates obtained from all possible orderings of streams in the SIC, the proposed method can provide improved performance with complexity comparable to conventional ordered SIC for moderate number of spatial streams.Index Terms-MIMO spatial multiplexing, maximum likelihood (ML) criterion, successive interference cancellation (SIC)

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“…To solve this problem, several complexity-reduced detection algorithms presenting close to full max-log-APP detection accuracy have been widely studied. Sphere detector [1], M-algorithm [2], LISS-algorithm [3], or variations of them [4]- [7] are some examples, still subjected to numerous BER performance-complexity trade-offs.…”

Section: Introductionmentioning

confidence: 99%

Survey on an efficient, low-complex tuple search based sphere detector

Adeva

Mennenga

Fettweis

2011

34th IEEE Sarnoff Symposium

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Nowadays, high detection complexity is known to be one of the major challenges in MIMO communications based on spatial multiplexing. Tuple search (TS) sphere detection was recently introduced, demonstrating to represent a promising approach in this context. It provides significant complexity reduction in comparison to conventional algorithms, providing in addition close to full max-log-APP BER performance. Due to the increasing multiplicity of communication standards as well as variety of mobile applications demanded by users, tackling the lack of flexibility of common receiver realizations has become an additional key challenge in MIMO detection. Aim of this work is to demonstrate that the benefit provided by the tuple search strategy is still present in a wide range of possible transmission schemes. For this purpose, a novel efficiency indicator is introduced, based on which an exhaustive analysis is performed. The existing tuple search detector has been adapted to deal with different constellation orders and transmit/receive antenna configurations. In addition, the applied MMSE strategy has been modified to support undetermined systems. The obtained results show the superiority of the proposed sphere detector under different transmission conditions, thus demonstrating its efficiency and flexibility.Keywords-MIMO detection, sphere decoder, tuple search, efficiency, overhead, asymmetric MIMO. I. INTRODUCTIONFuture mobile communication systems will make use of multiple-input multiple-output (MIMO) techniques in combination with high constellation orders to enhance spectral efficiency. Transmission of spatially multiplexed data streams allows increasing data rates as well as diversity. However, the inherent high detection complexity and lack of flexibility of common detector realizations still represent limiting factors towards efficient implementations. Tree search strategies have been shown to represent a promising approach to overcome the complexity problem. High order systems allow high data rates, but also imply great detection search spaces. As a consequence, detection algorithms exploring all or most of the possible sent symbols become impractical due to the resulting high computational complexity. This is the case of e.g. full max-log-APP detection. To solve this problem, several complexity-reduced detection algorithms presenting close to full max-log-APP detection accuracy have been widely studied. Sphere detector [1], M-algorithm [2], LISS-algorithm [3], or variations of them [4]-[7] are some examples, still subjected to numerous BER performance-complexity trade-offs.

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A Framework for Fixed Complexity Breadth-First MIMO Detection

Cited by 9 publications

References 22 publications

Designing Low-Complexity Detectors Based on Seysen's Algorithm

Designing Low-Complexity Detectors Based on Seysen's Algorithm

Improved MIMO SIC Detection Exploiting ML Criterion

Survey on an efficient, low-complex tuple search based sphere detector

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